1. A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba
- Author
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Dries De Witte, Ariel Alonso Abad, Geert Molenberghs, Geert Verbeke, Lizet Sanchez, Pedro Mas-Bermejo, Thomas Neyens, De Witte, Dries/0000-0003-3264-6984, De Witte , Dries, ALONSO ABAD, Ariel, MOLENBERGHS, Geert, VERBEKE, Geert, SANCHEZ, Lizet, Mas-Bermejo, Pedro, and NEYENS, Thomas
- Subjects
Joint models ,Infectious Diseases ,Multivariate spatio-temporal modeling ,Epidemiology ,Health, Toxicology and Mutagenesis ,Bayesian inference ,Geography, Planning and Development ,COVID-19 - Abstract
To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.
- Published
- 2023
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